Current Alzheimer’s disease (AD) diagnostics is based on clinical assessments, imaging and neuropsychological tests that are efficient only at advanced stages of the disease. Early diagnosis of AD will provide decisive opportunities for preventive treatment and development of disease-modifying drugs. Cerebrospinal fluid (CSF) is in direct contact with the human brain, where the deadly pathological process of the disease occurs. As such, the CSF biochemical composition reflects specific changes associated with the disease and is therefore the most promising body fluid for AD diagnostic test development. Here, we describe a new method to diagnose AD based on CSF via near infrared (NIR) Raman spectroscopy in combination with machine learning analysis. Raman spectroscopy is capable of probing the entire biochemical composition of a biological fluid at once. It has great potential to detect small changes specific to AD, even at the earliest stages of pathogenesis. NIR Raman spectra were measured of CSF samples acquired from 21 patients diagnosed with AD and 16 healthy control (HC) subjects. Artificial neural networks (ANN) and support vector machine discriminant analysis (SVM-DA) statistical methods were used for differentiation purposes, with the most successful results allowing for the differentiation of AD and HC subjects with 84% sensitivity and specificity. Our classification models show high discriminative power, suggesting the method has a great potential for AD diagnostics. The reported Raman spectroscopic examination of CSF can complement current clinical tests, making early AD detection fast, accurate, and inexpensive. While this study shows promise using a small sample set, further method validation on a larger scale is required to indicate the true strength of the approach.

当前的阿尔茨海默氏病（AD）诊断基于临床评估，影像学和神经心理学测试，这些测试仅在疾病的晚期才有效。AD的早期诊断将为预防性治疗和疾病改善药物的开发提供决定性的机会。脑脊液（CSF）与人脑直接接触，在那里发生致命的病理过程。因此，CSF生化成分反映了与疾病相关的特定变化，因此是AD诊断测试开发最有希望的体液。在这里，我们描述了一种基于CSF通过近红外（NIR）拉曼光谱结合机器学习分析来诊断AD的新方法。拉曼光谱法能够一次探测生物流体的整个生物化学组成。即使在发病的最早阶段，它也具有检测AD细微变化的巨大潜力。NIR拉曼光谱测量了从21名被诊断患有AD的患者和16名健康对照（HC）受试者采集的CSF样品。人工神经网络（ANN）和支持向量机判别分析（SVM-DA）统计方法用于区分目的，最成功的结果允许以84％的敏感性和特异性区分AD和HC对象。我们的分类模型显示出很高的判别力，表明该方法具有用于AD诊断的巨大潜力。报道的脑脊液拉曼光谱检查可以补充当前的临床检查，使早期的AD检测快速，准确且廉价。虽然这项研究显示了使用少量样本集的前景，但仍需要在更大范围内进行进一步的方法验证，以表明该方法的真正优势。